3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation. Huang, C., Han, H., Yao, Q., Zhu, S., & Zhou, K., S. Volume 1 , Springer International Publishing, 2019. ![3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper ![3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmenta- tion task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a sin- gle model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising univer- sal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U2-Net) is built upon sepa- rable convolution, assuming that images from different domains have domain-specific spatial correlations which can be probed with channel- wise convolution while also share cross-channel correlations which can be modeled with pointwise convolution. We evaluate the 3D U2-Net on five organ segmentation datasets. Experimental results show that this uni- versal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about 1% of the param- eters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U2-Net into public domain (https:// github.com/huangmozhilv/u2net
@book{
title = {3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation},
type = {book},
year = {2019},
source = {Proceeding of the International Conference on Medical Image Computing and Computer Assisted Interventions},
keywords = {Multi-domain learning,Segmentation,Universal model},
pages = {291-299},
volume = {1},
websites = {http://dx.doi.org/10.1007/978-3-030-32245-8_33},
publisher = {Springer International Publishing},
id = {dc353a48-a502-307f-a646-8928f5bbee35},
created = {2024-01-13T06:15:56.027Z},
file_attached = {true},
profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
last_modified = {2024-01-13T06:24:48.304Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {true},
hidden = {false},
private_publication = {false},
abstract = {Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmenta- tion task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a sin- gle model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising univer- sal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U2-Net) is built upon sepa- rable convolution, assuming that images from different domains have domain-specific spatial correlations which can be probed with channel- wise convolution while also share cross-channel correlations which can be modeled with pointwise convolution. We evaluate the 3D U2-Net on five organ segmentation datasets. Experimental results show that this uni- versal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about 1% of the param- eters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U2-Net into public domain (https:// github.com/huangmozhilv/u2net},
bibtype = {book},
author = {Huang, Chao and Han, Hu and Yao, Qingsong and Zhu, Shankan and Zhou, Kevin S.},
doi = {10.1007/978-3-030-32245-8}
}
Downloads: 0
{"_id":"4p8QRNQvoDFkriaAi","bibbaseid":"huang-han-yao-zhu-zhou-3du2neta3duniversalunetformultidomainmedicalimagesegmentation-2019","author_short":["Huang, C.","Han, H.","Yao, Q.","Zhu, S.","Zhou, K., S."],"bibdata":{"title":"3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation","type":"book","year":"2019","source":"Proceeding of the International Conference on Medical Image Computing and Computer Assisted Interventions","keywords":"Multi-domain learning,Segmentation,Universal model","pages":"291-299","volume":"1","websites":"http://dx.doi.org/10.1007/978-3-030-32245-8_33","publisher":"Springer International Publishing","id":"dc353a48-a502-307f-a646-8928f5bbee35","created":"2024-01-13T06:15:56.027Z","file_attached":"true","profile_id":"d5b9f595-4104-33d2-b640-e84dee1b7ded","last_modified":"2024-01-13T06:24:48.304Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"abstract":"Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmenta- tion task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a sin- gle model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising univer- sal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U2-Net) is built upon sepa- rable convolution, assuming that images from different domains have domain-specific spatial correlations which can be probed with channel- wise convolution while also share cross-channel correlations which can be modeled with pointwise convolution. We evaluate the 3D U2-Net on five organ segmentation datasets. Experimental results show that this uni- versal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about 1% of the param- eters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U2-Net into public domain (https:// github.com/huangmozhilv/u2net","bibtype":"book","author":"Huang, Chao and Han, Hu and Yao, Qingsong and Zhu, Shankan and Zhou, Kevin S.","doi":"10.1007/978-3-030-32245-8","bibtex":"@book{\n title = {3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation},\n type = {book},\n year = {2019},\n source = {Proceeding of the International Conference on Medical Image Computing and Computer Assisted Interventions},\n keywords = {Multi-domain learning,Segmentation,Universal model},\n pages = {291-299},\n volume = {1},\n websites = {http://dx.doi.org/10.1007/978-3-030-32245-8_33},\n publisher = {Springer International Publishing},\n id = {dc353a48-a502-307f-a646-8928f5bbee35},\n created = {2024-01-13T06:15:56.027Z},\n file_attached = {true},\n profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},\n last_modified = {2024-01-13T06:24:48.304Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmenta- tion task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a sin- gle model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising univer- sal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U2-Net) is built upon sepa- rable convolution, assuming that images from different domains have domain-specific spatial correlations which can be probed with channel- wise convolution while also share cross-channel correlations which can be modeled with pointwise convolution. We evaluate the 3D U2-Net on five organ segmentation datasets. Experimental results show that this uni- versal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about 1% of the param- eters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U2-Net into public domain (https:// github.com/huangmozhilv/u2net},\n bibtype = {book},\n author = {Huang, Chao and Han, Hu and Yao, Qingsong and Zhu, Shankan and Zhou, Kevin S.},\n doi = {10.1007/978-3-030-32245-8}\n}","author_short":["Huang, C.","Han, H.","Yao, Q.","Zhu, S.","Zhou, K., S."],"urls":{"Paper":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded/file/189bca1c-7aa3-1239-029c-a102aa8d4d9b/Li2019_Chapter_RecurrentAggregationLearningFo.pdf.pdf","Website":"http://dx.doi.org/10.1007/978-3-030-32245-8_33"},"biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","bibbaseid":"huang-han-yao-zhu-zhou-3du2neta3duniversalunetformultidomainmedicalimagesegmentation-2019","role":"author","keyword":["Multi-domain learning","Segmentation","Universal model"],"metadata":{"authorlinks":{}}},"bibtype":"book","biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","dataSources":["ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"],"keywords":["multi-domain learning","segmentation","universal model"],"search_terms":["net","universal","net","multi","domain","medical","image","segmentation","huang","han","yao","zhu","zhou"],"title":"3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation","year":2019}